Kernel Density Estimation =========================== .. _kernel_density_notebook: Link to Notebook GitHub _ .. raw:: html
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import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import mixture_rvs


#### A univariate example.¶

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np.random.seed(12345)

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obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm],
kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))

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kde = sm.nonparametric.KDEUnivariate(obs_dist1)
kde.fit()

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fig = plt.figure(figsize=(12,8))
ax.hist(obs_dist1, bins=50, normed=True, color='red')
ax.plot(kde.support, kde.density, lw=2, color='black');

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obs_dist2 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta],
kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5))))

kde2 = sm.nonparametric.KDEUnivariate(obs_dist2)
kde2.fit()

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fig = plt.figure(figsize=(12,8))
ax.hist(obs_dist2, bins=50, normed=True, color='red')
ax.plot(kde2.support, kde2.density, lw=2, color='black');


The fitted KDE object is a full non-parametric distribution.

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obs_dist3 = mixture_rvs([.25,.75], size=1000, dist=[stats.norm, stats.norm],
kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
kde3 = sm.nonparametric.KDEUnivariate(obs_dist3)
kde3.fit()

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kde3.entropy

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kde3.evaluate(-1)


#### CDF¶

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fig = plt.figure(figsize=(12,8))
ax.plot(kde3.support, kde3.cdf);


#### Cumulative Hazard Function¶

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fig = plt.figure(figsize=(12,8))
ax.plot(kde3.support, kde3.cumhazard);


#### Inverse CDF¶

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fig = plt.figure(figsize=(12,8))

fig = plt.figure(figsize=(12,8))